A Novel Adaptive Predictive Control Model for Handling Renewable Uncertainty in Hybrid PV–Wind–Battery Systems

Abstract

The increased penetration of renewable energy sources introduces significant uncertainty and variability in power systems, posing challenges for maintaining reliable energy management. This document presents the Adaptive Model Predictive Control (AMPC) method for photovoltaic (PV)–wind–battery hybrid energy in improving system performance under uncertain operating conditions. The proposed system was simulated and evaluated using MATLAB-based simulations over a 24-hour period with a nominal load of 100 kW. The hybrid system integrates photovoltaic (PV) and wind energy generation with a battery energy storage system (BESS) to address the mismatch between generation and load demand. Uncertainty in renewable energy generation is modeled as stochastic disturbances affecting the output of PV and wind. Simulation results show that the PV output peaks at around 70 kW during the day, while the wind turbine generates a relatively stable output in the range of 30–60 kW. The combination of these two energy sources can optimally meet the load demand of 90–110 kW during the period from 09:00 to 16:00. Compared to MPC, the AMPC method yields better performance with the ability to follow the load profile more accurately, reducing power mismatch errors to nearly 0 kW, and maintaining the Battery State of Charge (SoC) within a safe operating range of 50–90% with a faster charging process. The proposed approach provides a practical and scalable method for integrating renewable energy sources with energy storage systems, especially in environments characterized by high variability and uncertainty.